Effect of ASIS kernel. In Table REF , ASIS kernel advances GMIS kernel by 12.3% in iShape. We think that is because our well-designed affinity kernels based on dataset property can better discover the connectivity of different parts of an object. While the GMIS kernel suffers from its sparsity in distance and angle, examples are shown in Figure REF  and Figure REF  shows the reason for its failure through the affinity visualization. In Table REF , we also use ground truth affinity map to explore the upper bound of ASIS, where a 98.5% mAP is achieved, showing its great potential. Moreover, we find our non-centrosymmetric design of affinity kernel outperform centrosymmetric one by 10% in the table. We think such a design cut off the output and calculation redundancy and reduce the requirement of large receptive field from CNN, simplifying representation learning. Inspired by Grad-CAM [1]}, we use the gradient of the input image to visualize the model's activation map. As shown in Figure REF , ASIS pays more attention to the discriminative information such as the instance outline, while GMIS focuses more on the area around the center point.
